Communications in Computational Chemistry
The journal Communications in Computational Chemistry (CiCC) publishes short and rapid communications on all aspects of theoretical and computational chemistry. Regular, feature and perspective articles, reviews as well as software introductions are also acceptable.
Advances in Ultra-Coarse-Grained Models for Large Biomolecules
Recent advances in Ultra-Coarse-Graining (UCG) modeling for biological systems have improved both construction strategies and forcefield development. Empirical forcefields remain the primary choice for large systems due to their efficiency and ability to capture conformational dynamics, while non-restraining potentials and multiscale approaches have enhanced predictive capability for complex intermolecular interactions. Although bottom-up methods are currently limited by high parameterization costs, increasing physical interpretability and the growth of biomolecular trajectory databases are expected to make them more feasible and transferable in the future.
Copyright (c) 2026 Communications in Computational Chemistry
Optical Field Control of Ultrafast Dynamics in Complex Systems: Frontiers and Perspectives
Ultrafast optical field control has progressed from merely observing photochemical dynamics to actively steering molecular transformations. However, a persistent gap between theory and experiment continues to impede predictive control in complex systems. Femtosecond and attosecond techniques now allow real-time manipulation of electronic evolution, vibrational motions, and bond dissociation in small molecules. However, these achievements often fail to translate to condensed-phase environments due to increasing molecular complexity, environmental decoherence, and stringent instrumental constraints. This review summarizes recent advances in which pump-probe experiments—utilizing tailored pulse parameters such as intensity, wavelength, phase, and polarization—have uncovered key control mechanisms while also revealing critical challenges in scalability and reproducibility. Theoretical progress in quantum control methods (e.g., local control theory) and mixed quantum-classical simulations has clarified fundamental principles, yet often remains disconnected from experimental implementation. We propose that machine learning (ML) serves as an essential bridge to close this gap. By constructing hybrid theory-experiment databases and training environment-aware models, ML can capture complex pulse-branching correlations and system-environment couplings. These models effectively translate theoretical insights into experimentally feasible pulse designs, thereby paving the way for designing light-driven molecular processes that extend beyond gas-phase paradigms to functional materials.
State-Specific Treatment of Solvent Effect on Excited States in Organic Photoredox Catalysis
Excited state behavior in organic photocatalysis is strongly influenced by solvent polarity. We benchmark solvation treatments for predicting charge-transfer (CT) and locally excited (LE) states by combining time-dependent density functional theory (TDDFT) with implicit and explicit solvation. Within implicit models, linear-response (LR) and four state-specific (SS) schemes (cLR, ${\rm cLR}^2$ , VEM, IBSF) are assessed, and explicit solvation is modeled using QM/MM with electrostatic embedding. Across representative systems, we examine solvent-dependent excited state energetics to compare the solvation models. For CT states, SS approaches consistently capture solvent-induced stabilization, whereas LR underestimates solvent relaxation and misrepresents solvent trends. Among SS methods, cLR and ${\rm cLR}^2$ show nearly identical solvent dependence, and the additional transition density term in ${\rm cLR}^2$ vanishes for triplets and becomes relevant only when the singlet state contains appreciable LE character. VEM produces similar solvent dependence, while IBSF tends to over-stabilize CT states in polar solvents. In explicit solvation, QM/MM reproduces the SS trends with small, systematic shifts in absolute energies. By contrast, LE states exhibit weak solvent sensitivity and are consistently described by all methods. Separately, results across density functionals highlight the importance of long-range corrected hybrids for describing CT states. These results provide practical guidelines for selecting solvation models and exchange correlation functionals to reliably capture solvent effects in organic photocatalysis, particularly in systems with pronounced CT states.
Theoretical Study on the Inhibition Mechanism of the Proteasome by Bortezomib
Bortezomib can form covalent bonds with the proteasome to kill myeloma tumor cell, but the detailed covalent inhibition reaction mechanism remains unclear. In this study, we first established the parameters for boron atom in bortezomib and explored the possible reaction pathways of inhibition of the proteasome by bortezomib through molecular dynamics (MD) simulation and Quantum Mechanics/Molecular Mechanics (QM/MM) calculation. The computational results indicate that the most energetically favorable pathway includes two reaction steps. The first step involves a proton abstraction from the -OH group to ${\rm-NH_2}$ group of Thr1 residue, which is coupled with the nucleophilic attack on the boron atom of bortezomib by the newly formed ${\rm O}^-$ ion. The second step is the proton transfer from the protonated ${\rm-NH_3^+}$ group to one of the hydroxyl group connected to the boron atom, forming a bortezomib-proteasome complex associated with a water molecule coordinated with the boron atom. The rate-determining step of the inhibition reaction is the first step with an energy barrier of 19.1 kcal/mol, which is close to the activation energy of ~20.8 kcal/mol derived from kinetic experiments. This work provides detailed mechanistic insights for proteasome inhibition by the boron compounds.
Copyright (c) 2025 Communications in Computational Chemistry
Adsorption of Some Pteridine-Based Compounds on Fe (110) Surface and Potential for Corrosion Inhibition: Quantum Computations Molecular Dynamics Simulations
The adsorption of organic molecules onto steel surfaces has been a key strategy in the selection of materials for mitigation of corrosion in various aggressive media. Computational chemistry offers valuable insight into the electronic interactions that govern this process. In this study, five pteridine-based compounds, namely, isoxanthopterin, leucopterin, lumazine, pterin and xanthopterin, were assessed for their adsorption behaviour and corrosion inhibition potential on Fe(110) surface using density functional theory (DFT) and molecular dynamics (MD) simulations. Geometry optimization, frontier molecular orbital analysis, and quantum reactivity descriptors were computed using the B3LYP/DNP level of theory via the Dmol³ module in BIOVIA Material Studio. Fukui indices and Mulliken charge distributions were analyzed to predict adsorption sites. Additionally, adsorption energies and molecular configurations on the Fe(110) surface were examined using the Forcite and Adsorption Locator tools. Results showed that all the studied compounds exhibit planar geometries favorable for surface interaction, with isoxanthopterin and xanthopterin demonstrating the strongest adsorption energies. Key adsorption sites were localized around nitrogen and oxygen heteroatoms. The compounds are predicted to form stable interactions with the iron surface through both physisorption and chemisorption, indicating excellent potential for use as green corrosion inhibitors.
Combining All-Atom Molecular Dynamics Simulation and NMR to Analyze Conformational Ensemble of Intrinsically Disordered Proteins
Intrinsically disordered proteins (IDPs) lack stable tertiary structures and instead populate dynamic conformational ensembles, presenting unique challenges for structural characterization. In this review, we discuss the synergistic integration of all-atom molecular dynamics (MD) simulations and nuclear magnetic resonance (NMR) spectroscopy to elucidate the structural and dynamic properties of IDPs. NMR spectroscopy provides ensemble-averaged, site-specific structural and dynamic information, though its inherently sparse data limits resolution. Conversely, MD simulations yield atomically detailed trajectories but are constrained by sampling limitations and potential force field inaccuracies. Integrating both methods, using NMR data as restraints or reweighting criteria for MD simulations, improves accuracy and provides a more complete understanding of IDP behavior. Recent advancements include statistical reweighting techniques and AI-assisted methods to enhance sampling efficiency and ensemble construction. Despite progress, challenges remain in force field accuracy and seamless data integration. Future work will focus on improving force fields, developing more dynamic data integration methods, and leveraging AI for more efficient and accurate ensemble generation.
Unraveling Li-Ion Solvation Dynamics in Ether-Based Electrolytes via High-Throughput Molecular Dynamics Simulations and Machine Learning
Lithium-ion batteries (LIBs) are now widely used as energy storage systems in portable electronic devices and electric vehicles. The Li-ion solvation dynamics in electrolytes significantly influence the overall performance of LIBs. In this work, we performed high-throughput molecular dynamics smilations on thousands of ether-based electrolytes screened from a vast molecular database containing over 110 million organic molecules. Machine learning models combined with Shapley additive explanation analysis reveal that the presence of bulky alkyl groups reduce the solvent dipole moment, while enhancing the coordination ability of lithium ions with anions. Additionally, the introduction of ether-type groups (-O-) in the rings of cyclic ether molecules negatively affects the solvent dipole moment but positively influences the Li-anion coordination number. In contrast, ether-type groups (-O-) introduced in linear chains correlate positively with solvent dipole moment while negatively with the Li-anion coordination number. This work provides insights into the relation between molecular structure and Li-ion solvation dynamics in electrolyte solutions.
high-throughput molecular dynamics simulations
Breathing New Life into Classical Force Fields
Classical force fields form the foundation of molecular dynamics simulations. Because classical force fields are limited to pairwise-additive Lennard-Jones potentials and fixed-charge electrostatics, they cannot accurately capture explicit polarization and charge-transfer effects, metal coordination bonds, or weak, directional nonbonded interactions such as $π$–$π$ stacking. Machine-learning force fields and polarizable force fields, which have been claimed to accurately characterize these types of interactions, have developed rapidly in recent years, raising an ongoing debate over whether further improvements to classical force fields are still necessary. Keeping this question in mind, we review various strategies proposed in recent years to improve the description of nonbonded interactions for classical force fields. These advances include the development of atomic charge models that accurately reflect molecular dipoles, water models that capture electrostatic screening and solvation effects, atom-pair-specific van der Waals parameters to mimic polarization, refined atom types that better represent chemical environments, and the introduction of virtual sites for accurately modeling of lone pairs. We also discuss the applicable scope of each strategy. Furthermore, by comparing classical force fields with polarizable and machine-learning-based force fields, we identify three major advantages of classical force fields: high computational efficiency, strong transferability, and ease of parameterization. Therefore, we join the ongoing debate and believe that the community will greatly benefit from further development of classical force fields. Through the improvement of the accuracy of characterizing nonbonded interactions, classical force fields can be widely applied in areas such as million-atom simulations and high-throughput screening.
Computational Study on the Reaction Mechanism of LigW-Catalyzed Carboxylation of Monohydroxybenzoic Acids
Enzymatic carboxylation of phenols via the Kolbe-Schmitt reaction represents a promising sustainable strategy for $CO_2$ fixation and synthesis of high-value chemicals. This study investigates the reaction mechanism of 5-carboxyvanillate decarboxylase (LigW)-catalyzed carboxylation of non-natural monohydroxybenzoic acids, employing the quantum chemical cluster approach. First, we investigate the carboxylation mechanism of 4-hydroxybenzoate (4-HBA) to produce 4-hydroxyisophthalate (4-HIPA), a dicarboxylic acid with potent antioxidant and neuroprotective applications. The calculations reveal that the $CO_2$-binding mediates the preferred binding mode of the substrates and the incorporation of $CO_2$ to the active site favors the mode beneficial for the following reaction. The chemical reaction is initiated by the formation of a carbon-carbon bond between $CO_2$ and 4-HBA, followed by the rate-limiting proton transfer from the active site residue Asp314 to the resulting intermediate of the first step with a calculated barrier of 19.2 kcal/mol. Additionally, the potential of LigW in catalyzing the carboxylation of 3-hydroxybenzoate (3-HBA) is evaluated, and the calculations show that the reaction is energetically unfeasible due to the prohibitively high barrier of chemical steps. These mechanistic insights, together with the previous studies on the natural substrate, provide important information for the rotational design of LigW variants for industrial biocatalysis and $CO_2$ utilization.
Enhancing Optoelectronic Properties of Quasi-2D Ruddlesden–Popper Perovskites via Pseudo–Halogen Doping: A First–Principles Study on Cs₂Pb(SCN)₂Br₂
Two-dimensional (2D) Ruddlesden-Popper (RP) perovskites have been intensively investigated due to their superior stability and outstanding optoelectronic properties. Although A-site doping in quasi-2D RP-phase perovskites has been extensively studied, the effect of X-site doping remains unknown. Using first-principles calculations, this work demonstrates that ${\rm SCN}^-$ substitution in ${\rm Cs}_2{\rm Pb(SCN)}_2{\rm Br}_2$ induces a structural transformation from isotropic to anisotropic through octahedral tilting along the $b$-axis, reducing octahedral spacing from 5.17 to 4.88 Å. This structural modification enhances carrier mobility, dramatically increases exciton binding energy from 30.47 to 145.39 meV, and improves defect tolerance compared to pristine ${\rm Cs}_2{\rm PbBr}_4.$ These modifications synergistically suppress non-radiative recombination pathways while promoting radiative processes, so that improve its performance as a promising light-emitting diode (LED) material. These findings establish pseudo-halogen substitution as a promising strategy for optimizing carrier transport and radiative efficiency in low-dimensional perovskite LED devices.
Copyright (c) 2025 COPYRIGHT: © Global Science Press
First Principles Calculations on Atomistic Mechanisms of Surface/Interface-Induced Properties and Database Construction
Surfaces and interfaces play a critical role in exploring novel chemical and physical properties because the interactions between surface and adsorbents, as well as interfacial interactions, can be finely tuned by the interface configurations at the atomic level across a wide range, from strong covalent and ionic bonds to weak hydrogen bonds and van der Waals interactions. These interactions enable precise control over molecular adsorption, self-assembly, and activation, facilitating the design of nanostructures and modulation of novel properties. Recent developments in computational technologies, including high-throughput screening and machine learning, have significantly accelerated the discovery of functional materials. This review highlights theoretical progress in manipulating the physical and chemical properties of organic molecules at surfaces and interfaces, with a focus on controlling molecular configuration and magnetism, selective activation, on-surface synthesis, and interfacial engineering of 2D materials. Our goal is to understand the key factors influencing the physical and chemical properties at surfaces and interfaces, with a particular focus on theoretical insights and computational approaches.
First Theoretical Evidence of Blue-Shifted Energy Transfer in Bioluminescence
Energy transfer (ET) complex is not rare in bioluminescence. Usually, the ET occurs from the donor with higher emission energy to the acceptor with lower absorption energy. However, a blue-shifted ET is observed in the bioluminescence (BL) of Photobacterium phosphoreum (PP). The luminophore, 4a-hydroxy-5-hydro-flavin mononucleotide at the first singlet excited state ($S_1$-HFOH), in solitary PP luciferase (PPLuc) emits light at 495 nm. When a proportional concentration of lumazine protein (LumP) with a substrate of 6,7-dimethyl-8-ribityllumazine (DLZ) is introduced, the emission wavelength changes to 475 nm, accompanied by a 2.1-fold enhancement in intensity. The blueshift is only an observation, whose ET mechanism has not been uncovered over fifty years of research. In the present article, we evidenced that the ET process occurs via a Förster resonance energy transfer (FRET) mechanism by protein-protein docking and molecular dynamics (MD) simulations. Moreover, utilizing the combined quantum mechanics and molecular mechanics (QM/MM) method, we calculated the FRET rate and fluorescence quantum yield. The small Stokes shift of DLZ as well as the strong vibronic couplings of HFOH allow the blue-shifted FRET process. The calculated FRET rate is larger than the radiative and non-radiative decay ones of $S_1$-HFOH, and the fluorescence quantum yield of $S_1$-DLZ is higher than the one of $S_1$-HFOH, which clearly explains the experimentally observed enhancement of the emission intensity. Simultaneously, the blue-shifted FRET mechanism firstly interpreted that the wild-type PP emits 475 nm BL rather than 490 nm one as the other species of bioluminescent bacteria do. This first-time deep investigation establishes a theoretical research paradigm for the theoretical study of ET and holds significance in color regulation in the BL field.
AI4NLO: An Integrated Data Platform for Machine Learning-Driven Exploration of Inorganic Nonlinear Optical Materials
Nonlinear optical (NLO) materials, with their unique wavelength conversion capabilities, play a crucial role in a wide range of scientific and industrial applications. Despite significant progress, the development of novel NLO materials, particularly those in the deep ultraviolet and mid-infrared regions, remains a challenge. Recent advancements in machine learning (ML) technologies have injected new momentum into materials science research. In this work, we present an integrated data platform incorporating advanced ML techniques, designed to drive the discovery and exploration of inorganic NLO materials. The platform currently includes about 1000 entries with their structures and key properties. Users can apply built-in ML models developed in our group for immediate predictions of NLO properties or train their own models based on specific research needs. Additionally, the platform provides access to the results of deep generative models, allowing users to retrieve newly generated virtual crystal structures, thus expanding the chemical space for NLO materials exploration. This platform not only provides reliable data support for researchers but also holds the potential to accelerate the discovery of novel NLO materials.
Mechanism for the Inhibition of Dewetting in Polymer Thin Films by Interface Segregation of Single-Chain Nanoparticles
The inhibition of dewetting in polymer thin films (PTFs) is crucial for their practical applications. Through molecular dynamics simulations, we elucidate the mechanism of interface segregation of single-chain nanoparticles (SCNPs) in inhibiting PTF dewetting. Our study provides atomic-level insights into three key aspects: (1) how SCNP segregation affects the contact angle, (2) how it influences the orientation of matrix chains at the interface, and (3) how the interaction between solvent molecules and cross-linkers within SCNPs influences the dewetting behavior. By employing static droplet and dynamic pre-punched film models, we systematically investigate the effects of SCNP doping concentration and cross-linker-solvent interaction strength. Our results demonstrate that SCNP segregation significantly alters the orientation of linear polymer chains at the interface. In pure polymer films, chain ends preferentially accumulate at the interface due to entropic effects, leading to parallel chain conformations. However, SCNP segregation suppresses this effect, promoting more vertical chain orientations. Furthermore, we find that weaker cross-linker-solvent interactions promote SCNP segregation at the substrate interface, leading to more effective dewetting inhibition. These findings provide fundamental understanding and practical guidance for designing stable polymer thin films through nanoparticle doping.
Contributions of Vibronic Couplings to Photoelectron Spectroscopy in a Dimer: The Case of an Organic Mixed-Valence System
In the framework of a dynamic vibronic model, we report the influence of both local and nonlocal vibronic coupling on the vibronic energy levels for organic mixed-valence systems, and then evaluate their contributions to photoelectron spectroscopy of a realistic molecule as an example. Our results demonstrate that the variation of nonlocal vibronic coupling constant in the dimeric model doesn’t influence the relative positions of vibronic energy levels. For the spectrum simulated, it tries to maintain the constant difference $2t^0_{ab}$ between the splitting energy levels corresponding to two peaks of the first ionizations.
Catalytic Activity of Single-Atom Copper Modified Reconstructed Cerium Dioxide (100) Surface for Ammonia Oxidation: A DFT+U Study
Ammonia has been proposed as a potential carbon-free energy source. However, a highly active catalyst is required for ammonia oxidation to promote the combustion rate. In this study, the single-atom copper catalyst loaded on the reconstructed cerium dioxide (100) surface with the pocket-like structure ($Cu_1$/$CeO_4$-t-p) is constructed for ammonia oxidation, and the catalytic process is investigated using the density functional theory calculations corrected by on-site Coulomb interactions (DFT+U). The adsorptions of ammonia and oxygen, the dissociation of ammonia and the oxidation of the dissociated ammonia species are systematically examined.
reconstructed cerium dioxide (100) surface
Effect of Molecule Coverage on Nitric Oxide Reduction Reaction on Cu(111)
Electrochemical nitrogen oxide reduction reaction (NORR) can simultaneously remove atmospheric pollutant NO and produce the important chemical ammonia ($NH_3$), which, therefore, has garnered significant attention. However, the effect of molecule coverage on the catalyst surface on electrocatalytic activity is less discussed. In combination with atomic ab initio thermodynamics and first-principles calculations, the relationship between the NO coverage and catalytic NORR activity on Cu(111) is unraveled in this work. Results indicate that the adsorption stability and the limiting potential ($U_L$) of NORR on Cu(111) is closely related to NO coverage. In the case of standard conditions (1 atm, 300 K), NO adsorption with a coverage of 1/4 monolayer (ML) is the most stable configuration, though the corresponding $U_L$ (0.34 V) is higher than those of 1/9 (0.29 V) and 1/16 ML (0.29 V) adsorption while significantly lower than that of 1 ML (0.78 V). Therefore, our study provides insights into the role of temperature, pressure, and molecule coverage in electrochemical reactions.
Sodium Electron Solvation and Reactivity at Water Surface
Interfacial solvated electrons (${\rm e}^-_{{\rm sol}}s$) possess profound application values in physics, chemistry, and materials, thus attracting ever-growing attention. Although previous studies have unequivocally corroborated the involvement of ${\rm e}^-_{{\rm sol}}s$ in the reaction of alkali metals with water, the mechanism has not been thoroughly revealed. Here, we simulate the solvation and ionization process of a single Na or a metallic ${\rm Na}_8$ cluster at the vacuum-liquid interface by the hybrid functional-based $ab$ initio molecular dynamics (AIMD) method, especially to elucidate the interfacial electron dynamics behavior. Results show that the electron donated by Na or ${\rm Na}_8$ is partially solvated at the interface, a process driven by both the ${\rm Na}^+$ interaction with the electron and its stabilization in water, which promotes electron redistribution, delocalization, and activation. Additionally, solvation increases the $H_2O$ population near HOMO and on unoccupied orbitals, promoting $H_2O$ reorganization and electron transfer. In aqueous solutions, Na is highly ionized and generates a unique pre-solvated electron (${\rm e}^-_{{\rm pre}}$). ${\rm Na}_8$ cluster, on the other hand, is partially solvated through bottom active O-coordinating sites at the interface, polarizes internally, and produces a pre-solvated dielectron (${\rm e}^{2-}_{2 \ {\rm pre}}$), which is followed by $H_2O$ reorganization near the surface and the subsequent hydrogen evolution reaction by proton-coupled electron transfer. Surrounding $H_2O$ molecules form multiple Na-O bonds with the remaining ${\rm Na}^{2+}_8$ to compensate for ${\rm e}^{2-}_{2 \ {\rm pre}}$ loss. Our work displays the microscopic dynamics mechanism of Na and $H_2O$ reaction by AIMD simulation and provides evidence for the participation of ${\rm e}^-_{{\rm pre}}s$ in the hydrogen evolution reaction, which deepens our attention and understanding of redox reactions involving ${\rm e}^-_{{\rm sol}}s.$
$abinitio$ molecular dynamics simulation
Therapeutic Potential and Mechanistic Insights of Sunflower Receptacles’ Essential Oil Against Gout: An In-Depth Molecular Study
Gout is a common chronic disease caused by the deposition of monosodium urate crystals. Previous studies confirmed the anti-gout effects of sunflower receptacles. For example, eupatoriochromene may be one of the most important compounds for reducing uric acid and relieving gout, which can be extracted from the essential oil of sunflower receptacles (EOSR). However, the other active components in EOSR and their anti-gout molecular mechanisms remain unclear. In this work, we employed widespread methods such as network pharmacology, machine learning algorithm, molecular docking, and molecular dynamics simulation, to investigate the anti-gout molecular mechanisms of the EOSR components. The protein-protein interaction (PPI) network confirmed that the components of EOSR exert anti-gout effects mainly by targeting inflammatory targets. GO and KEGG enrichment analyses revealed that the components of EOSR play roles in several important biological pathways, potentially providing anti-gout effects through various mechanisms. Additionally, since the target URAT1 plays a critical role in the treatment of gout, we investigated the interactions between two components of EOSR, Linoleic acid (La) and Kauren-19-oic acid (Koa), and target URAT1 using machine learning algorithm, molecular docking and molecular dynamics simulation. It confirmed that La and Koa can stably bind to URAT1 and shift its conformation to the Inward-facing state. Similar to the positive control Benzbromarone (Ben), both La and Koa induce secondary structural changes in URAT1, with Koa sharing key residues with Ben. This research further indicates the molecular mechanisms of EOSR in treating gout and expands the range of therapeutic agents for it.
Energy Near-Degeneracy Driven Covalency Analyzed by a Two-Electron Two-Orbital Model
A simple model based on the two-electron two-orbital textbook problem is presented and used to analyze pairwise interatomic interactions in metal-ligand bonding. In particular the two types of covalency discussed during the last decade for actinide-ligand interactions, overlap/interaction driven and energy-near-degeneracy driven covalency, as well as their influence on the bond strengths and interatomic charge build-up are discussed. The hydration complexes ${\rm M}({\rm H}_2{\rm O})^{4+}_n$ of selected tetravalent lanthanide and actinide ions are used to probe the performance of the model for an analysis of calculations as well as for predictions.
Benchmarking Universal Machine Learning Force Fields with Hydrogen-Bonding Cooperativity
Machine learning force fields (MLFFs) offer a promising balance between quantum mechanical (QM) accuracy and molecular mechanics efficiency. While MLFFs have shown strong performance in modeling short-range interactions and reproducing potential energy surfaces, their ability to capture long-range cooperative effects remains underexplored. In this study, we assess the ability of three MLFF models — ANI, MACE-OFF, and Orb — to reproduce cooperative interactions arising from environmental induction and dispersion, which are essential for many biomolecular processes. Using a recently proposed framework, we quantify hydrogen bond (H-bond) cooperativity in N-methylacetamide polymers. Our results show that all MLFFs capture cooperativity to some extent, with MACE-OFF yielding the closest agreement with QM data. These findings highlight the importance of evaluating many-body effects in MLFFs and suggest that H-bond cooperativity can serve as a useful benchmark for improving their physical fidelity.
DFT Study on the Mechanism of Water Oxidation Catalyzed by a Mononuclear Copper Complex
This work employed DFT calculations to elucidate the mechanism of water oxidation reaction catalyzed by a mononuclear pyridine-based copper complex, which was reported to be a homogeneous water oxidation catalyst in a pH=11.84 buffer solution. The coordination of one water molecule with the ${\rm Cu}^{{\rm II}}$ center leads to the generation of the ${\rm Cu}^{{\rm II}}-{\rm OH}_2 (1-{\rm H}_2{\rm O}).$ The active species $({\rm Cu}^{{\rm IV}}={\rm O, 3})$ is generated after two subsequent proton-coupled electron transfer processes from $1-{\rm H}_2{\rm O}.$ $3$ triggers the O-O bond formation via water nucleophilic attack mechanism. The triplet ${\rm O}_2$ can be released after following two oxidation processes. The formation of the O-O bond is the rate-determining step for the catalytic cycle associated with a total barrier of 19.3 kcal/mol.
Two-Dimensional Convolutional Neural Networks for Predicting Non-Markovian Quantum Dissipative Dynamics
Simulating non-Markovian quantum dissipative dynamics remains a major challenge in theoretical and computational chemistry. While traditional numerical methods such as hierarchical equations of motion are numerically exact, they suffer from prohibitive computational costs when modeling systems with complex environmental couplings or strong non-Markovian effects. To address this limitation, we propose a deep learning framework based on two-dimensional convolutional neural networks (2D-CNN) for efficiently predicting long-time quantum dissipative dynamics using only short-time trajectory data. Our approach incorporates a 1D-to-2D feature reconstruction strategy, which transforms 1D time-series data into 2D images, and a multi-timescale fusion network to resolve complex dynamical features. We validate the framework on two paradigmatic cases — dissipative relaxation in a two-level system and Rabi oscillations in a dissipative spin system — achieving prediction mean absolute errors of $10^{-3}$ and $10^{-2},$ respectively. The results highlight the effectiveness of our 2D-CNN approach in capturing long-time temporal correlations, providing a computationally efficient pathway for simulating quantum dynamics in realistic open systems.
Molecular Insight into Energy Loss in Organic Solar Cells
With the rapid development of A-D-A non-fullerene acceptors, organic solar cells (OSCs) have made significant progress. However, compared to inorganic and perovskite solar cells, the energy loss in OSCs remains relatively large. In this Perspective, we summarize our recent computational advances in elucidating the mechanisms of energy loss in OSCs at the molecular level. We highlight strategies to minimize voltage loss during charge generation, suppress triplet-channel recombination, and reduce non-radiative voltage loss by modulating both molecular and aggregation structures.
Evaluation of Phase Networks in Transformer-Based Neural Network Quantum States
Neural network quantum states represent a powerful approach for solving electronic structures in strongly correlated molecular and material systems. For a neural network ansatz to be accurate, it must effectively learn the phase of a complex wave function. In this work, we demonstrate several different network structures as the phase network for a Transformer-based neural network quantum state implementation. We compare the accuracy of ground state energies, the number of parameters, and computational time across several small molecules. Furthermore, we propose a phase network setup that combines cross attention and multilayer perceptron structures, with the number of parameters remaining constant across different systems. Such an architecture may help reduce computational costs and enable transfer learning to larger quantum chemical systems.
Unraveling the Impact of Non-Covalent Interactions and Different Donor Moieties on Charge Transport in DPPT-Based Polymers
The integration of both rigid and flexible components holds great potential to significantly enhance the overall performance of organic electronic devices. Non-covalent interactions are frequently harnessed to augment the planar conjugation of polymers, consequently elevating the rigidity of these polymers. However, the influence of the dihedral angle distortion between donor and acceptor units, which is induced by the flexibility inherent in donor-acceptor copolymers, on charge transport remains poorly understood. In this study, we systematically investigate intra-chain charge transport parameters and charge mobility for the 3,6-bis(thiophen-2-yl) diketopyrrolopyrrole (DPPT) conjugated with various donor moieties. Combining with density functional theory (DFT) and the Su-Schrieffer-Heeger (SSH) model, we find that when the non-covalent interactions between the donor and acceptor units are enhanced (as exemplified by DPPT-FT and DPPT-BO), the coupling between electrons and low-frequency vibrations is significantly suppressed. Simultaneously, the intra-chain electronic coupling increases owing to substantial orbital overlap. Surface hopping simulations are utilized to study the charge transport properties. For DPPT-T, DPPT-FT, and DPPT-BT, weaker molecular rigidity and disordered chain packing lead to thermally activated hopping transport (low electronic coupling and high reorganization energy). In contrast, the enhanced structural rigidity of DPPT-BO facilitates charge delocalization, leading to an initial improvement in carrier mobility under low-temperature conditions, and thermal fluctuation effects induce a band-like behavior at high temperature.
Mechanistic Insights into the Pd-Catalyzed Carbonylation of Alkynol for $α$-Methylene-$β$-Lactone Formation
Density functional theory (DFT) calculations were performed to elucidate the reaction mechanism of the Pd-catalyzed carbonylation of propargylic alcohol (1), leading to the efficient synthesis of cyclohexyl $α$-methylene-$β$-lactone (2). Our study revealed that the reaction proceeds through a four-step pathway: alkyne migration and insertion, CO insertion, HCl-assisted hydrogen transfer, and final C–O annulation. Notably, the final C–O annulation step was identified as the rate-determining step (RDS) of the overall catalysis, with a free energy barrier of 25.7 kcal/mol (i.e., $\mathbf{IM4}→\mathbf{TS4}).$ Additionally, we uncovered the critical role of the HCl during the reaction pathway, a demonstrating that it acts as a co-catalyst, proton shuttle, and hydrogen bond donor/acceptor. NBO, EDA-NOCV, and HIGM analyses further revealed that the remarkable stability of the transition state $\mathbf{TS3}$ in the presence of HCl primarily arises from strong electrostatic attraction and orbital interaction energies between the two interacting fragments. These mechanistic insights provide valuable insight and guidance for the rational design of new Pd-catalyzed transformations.
Insights into the Chalcogen Bonding Catalysis on the ${\rm CO}_2$ Fixation with Styrene Oxide
The fixation of ${\rm CO}_2$ is a promising carbon-neutral approach and drawing lots of attention in the past decades. Among lots of fixation of ${\rm CO}_2$ methods, the cycloaddition of ${\rm CO}_2$ into epoxides (CCE) reaction is important because it could generate high value-added products. Recently, employing chalcogen bonding (ChB) catalysts for the CCE reaction has been proposed and offers significant advantages of low price, environmental friendliness, and ease of recycling. In this work, the ChB catalyzed the CCE reaction is investigated by high level theoretical calculations. The reaction can be divided into three subprocesses: ring-opening, nucleophilic addition, and formation of cyclic carbonate. Both Se- and Te-based ChB catalysts have shown the potential for catalyzing the CCE reaction, the overall catalytic performance of Te-based catalysts was superior to the Se-based ones. Overall, the most positive electrostatic potentials of the ChB catalysts, electrostatic term of the ChB binding energies, electron density at the ChB critical points, and electron density difference play important roles in the ChB catalyzed CCE reaction. This work elucidated the ChB catalyzed mechanism of the CCE reaction and provided theoretical guidance for the future development of efficient ChB catalysts for the CCE reaction.
Quantum Dynamics of Water from Møller-Plesset Perturbation Theory via a Neural Network Potential
We report the static and dynamical properties of liquid water at the level of second-order Møller-Plesset perturbation theory (MP2) with classical and quantum nuclear dynamics using a neural network potential. We examined the temperature-dependent radial distribution functions, diffusion, and vibrational dynamics. MP2 theory predicts over-structured liquid water as well as a lower diffusion coefficient at ambient conditions compared to experiments, which may be attributed to the incomplete basis set. A better agreement with experimental structural properties and the diffusion constant are observed at an elevated temperature of 340 K from our simulations. Although the high-level electronic structure calculations are expensive, training a neural network potential requires only a few thousand frames. This approach shows great potential, requiring modest human effort, and is straightforwardly extensible to other simple liquids.